1. Field of the Invention
The present invention relates to the field of medical devices and methods for processing cardiac signals.
2. Description of the Related Art
Electrocardiogram (ECG or EKG) recording is a valuable tool for physicians to study patient heart conditions. In a typical 12-lead arrangement, up to 12 sensors are placed on a subject's chest or abdomen and limbs to record the electric signals from the beating heart. Each sensor, along with a reference electrode, forms a separate channel that produces an individual signal. The signals from the different sensors are recorded on an ECG machine as different channels. The sensors are usually unipolar or bipolar electrodes or other devices suitable for measuring the electrical potential on the surface of a human body. Since different parts of the heart, such as the atria and ventricles, produce different spatial and temporal patterns of electrical activity on the body surface, the signals recorded on the ECG machine are useful for analyzing how well individual parts of the heart are functioning.
A typical heartbeat signal has several well-characterized components. The first component is a small hump in the beginning of a heartbeat called the “P-Wave”. This signal is produced by the right and left atria. There is a flat area after the P-Wave which is part of what is called the PR interval. During the PR interval the electrical signal is traveling through the atrio-ventricular node (AV) node. The next large spike in the heartbeat signal is called the “QRS Complex.” The QRS Complex is a tall, spiked signal produced by the ventricles. Following the QRS complex is another smaller bump in the signal called the “T-Wave,” which represents the electrical resetting of the ventricles in preparation for the next signal. When the heart beats continuously, the P-QRS-T waves repeat over and over.
The measurements can be used to determine such features as the underlying rate and rhythm mechanism of the heart, the orientation of the heart (how it is placed) in the chest cavity, evidence of increased thickness (hypertrophy) of the heart muscle, evidence of damage to the various parts of the heart muscle (myocardium), evidence of acutely impaired blood flow to the heart muscle, or patterns of abnormal electric activity that may predispose the patient to abnormal cardiac rhythm disturbances. More specifically, such information can identify abnormally fast (tachycardia) slow (bradycardia) or irregular heart rhythms (arrhythmias), abnormal conduction of cardiac impulses—which may suggest underlying cardiac or metabolic disorders, occurrence of a prior heart attack (myocardial infarction), an evolving and/or acute heart attack, an acute impairment to blood flow to the heart during an episode of a threatened heart attack (unstable angina or coronary artery disease), adverse effects on the heart from various heart diseases or systemic diseases (such as high blood pressure, thyroid conditions, heart valve diseases, dilated cardiomyopathy, or other myocardial defects, etc.), adverse effects on the heart from certain lung conditions (such as emphysema, pulmonary embolus (blood clots to lung), etc.), certain congenital heart abnormalities, abnormal blood electrolytes (e.g., potassium, calcium, magnesium), inflammation of the heart or its lining (myocarditis, pericarditis).
The rhythm analysis first determines the origins of the predominant rhythm in the sample and chooses from the major categories consisting of electronic atrial pacing, atrial flutter, ectopic atrial rhythm, sinus rhythm, junction rhythm and atrial fibrillation, including asystole, tachycardia, trigeminy, bradycardia, bigeminy and atrial and ventricular fibrillations, pacemakers, implantable cardioverter defibrillator, electrical alternans—pericardial effusion, and the like. The morphology interpretation will determine the existence of ischaemic heart diseases and syndromes such as Wolff-Parkinson-White, Long QT interval Romano-Ward Syndrome and Lown-Ganong-Levine Syndrome, hypertrophy patterns such as stenosis, QRS abnormalities such as low voltage QRS, pulmonary disease pattern, QRS axis, conduction abnormalities, ventricular hypertrophy, infarction, ST+T abnormality with ventricular hypertrophy, dating infarcts, epicardial injury, pericarditis, early repolarization, nonspecific ST elevation, subendocardial injury, nonspecific ST depression, digitalis effect, junctional ST depression, ischemia, QRS-T angle and QT interval, atrioventricular (AV) block, acute pulmonary embolus, bundle branch block, hyper- and hypo-kalaemia, piggy-back heart transplant, digitalis effect, ST deviation, and other normal and abnormal signals.
Many publications have described studying cardiac signals and detecting abnormal heart conditions. Sample publications include U.S. Patent Publication No. 20020052557; Podrid & Kowey, Cardiac Arrhythmia: Mechanisms, Diagnosis, and Management Lippincott Williams & Wilkins Publishers (2nd edition, Aug. 15, 2001); Marriott & Conover, Advanced Concepts in Arrhythmias, Mosby Inc. (3rd edition, Jan. 15, 1998); and Josephson, M. E., Clinical Cardiac Electrophysiology: Techniques and Interpretations, Lippincott Williams & Wilkins Publishers; ISBN (3rd edition, Dec. 15, 2001).
Unfortunately, although ECG signals have been studied for decades, they are difficult to assess because ECG signals recorded at the surface of the skin or at the surface of the heart are mixtures of signals from multiple sources. Typically, it is relatively straightforward to measure the shape of the QRS complex since this signal is so strong. However, irregular shaped P-wave or T-wave signals, along with weak irregular oscillatory signals that suggest a heart arrhythmia are often masked by large pacemaker signals, or the strong QRS complex signals. Thus, it can be very difficult to isolate small irregular oscillatory signals and to identify arrhythmia conditions.
In addition, atrial and ventricular signals are sometimes undesirably superimposed over one another. In many cases, diagnosis of disease states requires these signals to be separated from one another. For example, it might be desirable to separate P wave signals from QRS complex signals, so that signals originating in an atrium are isolated from signals representing concurrent activities in the ventricle.
In some practices the ECG signals are electronically “filtered” by excluding signals of certain frequencies. Typically, filters are applied to ECG signals to remove extraneous signals or disturbances, such as those due to baseline drift, power line interference and interferences from other physiological sources. The signals may also be “averaged” to remove largely random or asynchronous data, which is assumed to the meaningless “noise.” The filtering and averaging methods irreversibly eliminate portions of the recorded signals, including information that may be important to an accurate diagnosis. In addition, it is not known whether the more random data is truly “noise” and thus meaningless to an evaluation of the heart's condition. It might be that the signals removed by filtering are indicative of a disease state in a patient. One filtering method is disclosed in U.S. Pat. No. 6,308,094 entitled “System for prediction of cardiac arrhythmias,” which uses Karhunen Loeve Transformation to reduce or compress cardiac signals into elements that are deemed “significant.” As a result, the information that is deemed “insignificant” is lost.
Compared to other signal separation applications, separating ECG recording signals presents additional challenges. For example, the sources are not always stationary since the heart chambers contract and expand during beating. Additionally, the activity of a single chamber may be mistaken for multiple sources because of the presence of moving waves of electrical activity across the heart. If electrodes are not securely attached to the patient, or if the patient moves (for example older patients may suffer from uncontrolled jittering), the movement of the electrodes also undesirably generates signals. In addition, multiple signals can be sensed by the ECG which are unrelated to the cardiac signature, such as myopotentials, i.e., electrical signals from muscles other than the heart.
In addition, typically up to 12 leads of sensors are placed on the chest, torso, limbs, abdomen and/or back of a patient to enable the recording of multiple signals. Since each sensor generates one channel of an electronic signal, multiple sensors accommodate the recording of multiple signals. The signals are processed in the electrocardiogram device, and a display or recording is generated for use by medical personnel. The medical personnel, using their skills and training, evaluate the results to find and diagnose an abnormal cardiac signal. By using multiple channels of signals, the electrocardiogram can be used to identify or isolate abnormal heartbeats for evaluation and diagnostic purposes. However, it is time consuming and cumbersome to place multiple sensors and route their associated leads to the electrocardiogram. Further, such multi-lead packs are costly, may not be appropriately used in temporary or emergency situations, and impractical for medical personnel unless read through a long-term ECG recording for labeling abnormal heartbeats. When a 12-lead system can not be used, fewer leads may be applied, but there may be an associated decrease in the quality and detail in the resulting electrocardiogram recording. For example, a 3-lead sensor such as a Holter system may be used, typically to record the cardiac condition of a patient who is away from a hospital bed or stays at home.
Unfortunately, each channel (lead) of recorded ECG signals is typically a combination of signals from multiple sources originating from events occurring at different compartments of the heart, and strong signals such as QRS complex signals typically dominate other signals. Unwanted signals from other sources, such as signals generated by pacemakers or by non-cardiac muscle movements (such as the trembling of an elderly patient), may be included and combined in the ECG recordings. Therefore, it is desirable to separate the ECG signals into components of independent sources so that the separated components can be used for medical analysis of the patient's condition. For example, copending U.S. patent application Ser. No. 10/482,931, entitled “System and Method for Separating Cardiac Signals”, discloses a system for separating a cardiac signal into its independent sources by using an independent component analysis process, and is incorporated herein by reference. Once separated, the components may be displayed or otherwise used for diagnosis and treatment.
Devices with 1 or more sensors are used in various scenarios, such as automated external defibrillators or other situations when the more robust 12-lead sensors can not be used. Compared to 12-lead, devices with less sensors are more affordable, easier to store, and easier to use. However, fewer sensors typically provide less detailed cardiac information, and are therefore useful for general evaluation only. Since only limited information may be derived from fewer lead systems, an incorrect or incomplete evaluation and diagnosis may be made, which may result in long-term medical complications or even death. Accordingly, the fewer lead system is typically replaced with a multiple-lead system (preferably a 12-lead system) as soon as practical.